The invention relates generally to text processing, and more particularly to sentiment-bearing text processing.
Web users contribute a significant amount of content such as user reviews for various products and services, which are commonly found on shopping sites, weblogs, forums, etc. Such review data reflect Web users' sentiment toward products and are very helpful for consumers, manufacturers, and retailers. Various types of classification of such reviews are performed to analyze such review data. A typical type of classification is sentiment classification, wherein reviews are categorized to represent the sentiments of the users. Another type of such classification is intent classification or intent mining.
Sentiment classification of online product reviews has been drawing an increase in attention. Typical sentiment categories include, for example, positive, negative, mixed, and none. “Mixed” implies that a review contains both positive and negative opinions. “None” implies that there are no user opinions conveyed in the user review. Sentiment classification can be applied to classifying product features, review sentences, an entire review document, or other writing.
On the other hand, intent mining is a document analysis wherein a willingness of an author to perform an action is analyzed. Intent mining analyzes grammatical patterns that express intent. However, the process of intent mining is complex due to multiple modes of expressing intent. Furthermore, vocabulary for expressing intent is not well-defined.
Hence, there is a need for an improved intent mining process to analyze Web user reviews.